204 research outputs found
The non-smooth and bi-objective team orienteering problem with soft constraints
In the classical team orienteering problem (TOP), a fixed fleet of vehicles is employed, each of them with a limited driving range. The manager has to decide about the subset of customers to visit, as well as the visiting order (routes). Each customer offers a different reward, which is gathered the first time that it is visited. The goal is then to maximize the total reward collected without exceeding the driving range constraint. This paper analyzes a more realistic version of the TOP in which the driving range limitation is considered as a soft constraint: every time that this range is exceeded, a penalty cost is triggered. This cost is modeled as a piece-wise function, which depends on factors such as the distance of the vehicle to the destination depot. As a result, the traditional reward-maximization objective becomes a non-smooth function. In addition, a second objective, regarding the design of balanced routing plans, is considered as well. A mathematical model for this non-smooth and bi-objective TOP is provided, and a biased-randomized algorithm is proposed as a solving approach. © 2020 by the authors.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness & FEDER (SEV-2015-0563), the Spanish Ministry of Science (PID2019-111100RB-C21, RED2018-102642-T), and the Erasmus+ Program (2019-I-ES01-KA103-062602)
Electric vehicle routing, arc routing, and team orienteering problems in sustainable transportation
[EN] The increasing use of electric vehicles in road and air transportation, especially in last-mile delivery and city mobility, raises new operational challenges due to the limited capacity of electric batteries. These limitations impose additional driving range constraints when optimizing the distribution and mobility plans. During the last years, several researchers from the Computer Science, Artificial Intelligence, and Operations Research communities have been developing optimization, simulation, and machine learning approaches that aim at generating efficient and sustainable routing plans for hybrid fleets, including both electric and internal combustion engine vehicles. After contextualizing the relevance of electric vehicles in promoting sustainable transportation practices, this paper reviews the existing work in the field of electric vehicle routing problems. In particular, we focus on articles related to the well-known vehicle routing, arc routing, and team orienteering problems. The review is followed by numerical examples that illustrate the gains that can be obtained by employing optimization methods in the aforementioned field. Finally, several research opportunities are highlighted.This work has been partially supported by the Spanish Ministry of Science, Innovation, and Universities (PID2019-111100RB-C21-C22/AEI/10.13039/501100011033, RED2018-102642-T), the SEPIE Erasmus+Program (2019-I-ES01-KA103-062602), and the IoF2020-H2020 (731884) project.Do C. Martins, L.; Tordecilla, RD.; Castaneda, J.; Juan-Pérez, ÁA.; Faulin, J. (2021). Electric vehicle routing, arc routing, and team orienteering problems in sustainable transportation. Energies. 14(16):1-30. https://doi.org/10.3390/en14165131130141
The Vehicle Routing Problem with Service Level Constraints
We consider a vehicle routing problem which seeks to minimize cost subject to
service level constraints on several groups of deliveries. This problem
captures some essential challenges faced by a logistics provider which operates
transportation services for a limited number of partners and should respect
contractual obligations on service levels. The problem also generalizes several
important classes of vehicle routing problems with profits. To solve it, we
propose a compact mathematical formulation, a branch-and-price algorithm, and a
hybrid genetic algorithm with population management, which relies on
problem-tailored solution representation, crossover and local search operators,
as well as an adaptive penalization mechanism establishing a good balance
between service levels and costs. Our computational experiments show that the
proposed heuristic returns very high-quality solutions for this difficult
problem, matches all optimal solutions found for small and medium-scale
benchmark instances, and improves upon existing algorithms for two important
special cases: the vehicle routing problem with private fleet and common
carrier, and the capacitated profitable tour problem. The branch-and-price
algorithm also produces new optimal solutions for all three problems
Practical Route Planning Algorithm
Routing algorithms are traditionally considered to apply thesum of profits gathered at visited locations as an objectivefunction since the Traveling Salesman Problem. This heritagedisregards many practical considerations, hence the result ofthese models meet with user’s needs rarely.Thus considering the importance of this theoretical and modelingproblem, a novel objective function will be presented inthis paper as an extension of the one inherited from the TSPthat is more aligned with user preferences and aims to maximizethe tourist’s satisfaction. We also propose a heuristicalgorithm to solve the Team Orienteering Problem with relativelylow computation time in case of high number of verticeson the graph and multiple tour days. Based on the key performanceindicators and user feedback the algorithm is suitableto be implemented in a GIS application considering that even a3-day tour is designed less than 4 seconds
Solving Multi‑Objective Team Orienteering Problem with Time Windows Using Adjustment Iterated Local Search
One of the problems tourism faces is how to make itineraries more effective and efficient. This research has solved the routing
problem with the objective of maximizing the score and minimizing the time needed for the tourist’s itinerary. Maximizing
the score means collecting a maximum of various kinds of score from each destination that is visited. The profits differ
according to whether those destinations are the favorite ones for the tourists or not. Minimizing time means traveling time
and visiting time in the itinerary being kept to a minimum. Those are small case with 16 tourism destinations in East Java,
and large case with 56 instances consists of 100 destinations each from previous research. The existing model is the Team
Orienteering Problem with Time Window (TOPTW), and the development has been conducted by adding another objective,
minimum time, become Flexible TOPTW. This model guarantees that an effective itinerary with efficient timing to implement
will be produced. Modification of Iterated Local Search (ILS) into Adjustment ILS (AILS) has been done by replacing
random construction in the early phase with heuristic construction, continue with Permutation, Reserved and Perturbation.
This metaheuristic method will address this NP-hard problem faster than the heuristic method because it has better preparation
and process. Contributing to this research is a multi-objective model that combines maximum score and minimum time,
and a metaheuristics method to solve the problem faster and effectively. There are calibration parameter with 17 instances
of 100 destinations each, small case test using Mixed Integer Linear Programming, and large case test comparing AILS
with Multi-Start Simulated Annealing (MSA), Simulated Annealing (SA), Artificial Bee Colony (ABC), and Iterated Local
Search. The result shows that the proposed model will provide itinerary with less number of visited destination 4.752% but
has higher total score 8.774%, and 3836.877% faster, comparing with MSA, SA, and ABC. While AILS is compared with
ILS, it has less visited destination 5.656%, less total score 56.291%, and faster 375.961%. Even though AILS has more efficient
running time than other methods, it needs improvement in algorithm to create better result
The Role of Metaheuristics as Solutions Generators
Optimization problems are ubiquitous nowadays. Many times, their corresponding
computational models necessarily leave out of consideration several characteristics and features of
the real world, so trying to obtain the optimum solution can not be enough for a problem solving
point of view. The aim of this paper is to illustrate the role of metaheuristics as solutions’ generators
in a basic problem solving framework. Metaheuristics become relevant in two modes: firstly because
every run (in the case of population based techniques) allows to obtain a set of potentially good
solutions, and secondly, if a reference solution is available, one can set up a new optimization problem
that allows to obtain solutions with similar quality in the objectives space but maximally different
structure in the design space. Once a set of solutions is obtained, an example of an a posteriori
analysis to rank them according with decision maker’s preferences is shown. All the problem solving
framework steps, emphasizing the role of metaheuristics are illustrated with a dynamic version of
the tourist trip design problem (for the first mode), and with a perishable food distribution problem
(for the second one). These examples clearly show the benefits of the problem solving framework
proposed. The potential role of the symmetry concept is also exploredProject PID2020-112754GB-I00 from MCINAEI/10.13039/
501100011033
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